Dynamic job shop scheduling performance evaluation based on green intelligent manufacturing and thermal efficiency improvement

As the global concern for sustainable development continues to deepen, green intelligent manufacturing as an important means to improve production efficiency and reduce environmental impact, the improvement of thermal efficiency is an important link to achieve green manufacturing. In this study, dynamic job-shop scheduling is used to improve thermal energy efficiency, thereby optimizing overall production performance and promoting the practice of green intelligent manufacturing. Based on the system dynamics model and the actual production data, the current situation of heat energy consumption in the workshop was analyzed. By establishing scheduling optimization algorithm, the work order and resource allocation in the workshop can be dynamically adjusted to maximize the efficiency of heat energy use. And the performance evaluation index system is introduced to comprehensively evaluate the scheduling effect. After optimized scheduling, the thermal energy efficiency of the workshop is improved, the production cycle is shortened, the waste heat recovery rate is also significantly increased, and the overall production cost is reduced. Through dynamic job shop scheduling, the thermal energy efficiency is effectively improved, creating conditions for the realization of green intelligent manufacturing.